Project Objective

The objective of this project is to analysis out where the free/open hotspots and roadway sections are. We are able to identify the locations and routes that have the best internet infrastructure for testing new services as a consequence. My use case will be Koln.

If you have access to the roadway sections with the necessary internet infrastructure, it will be easier to choose the sites for new services like speed monitoring, traffic light smart atomation, or any IOT application linked to smart cities. With the help of this research, it will be possible to test new technologies without worrying about setting up the required infrastructure.

Data science Question :

Which intersections in Kolen have access to a hotspot WIFI?

Import libraries

Load Data

Our Dataset consists of two tables :

Table name Short discription
Hotspots in Köln List of urban hotspots in Köln. In addition to some information regarding this hotspots like the geometrical location , the house number and street name.
Road sections Köln The Köln street directory provides an overview of all applicable street names , addresses , geometry paths and house numbers in each road section

Visulaize Data

To get an overview of the Dataset, i will visualize an overview of our tables. This overview contain the most important infromation in each table

Data preparation

We need to prepare the data before we proceed with our solutions to the data science challenge.

In our database, the street names serve as the primary key. Unfortunately, the tables don't have standardized street names; for instance, they may finish in XYZ str, XYZ strasse, or other variations. Due to this, merging the two tables and processing the data to find a solution is challenging.

So , the steps of data preparation :

Algorithms and solution

First solution :

We filter the road section that has house number that directly have a hotspot

Seconed solution :

we filter the road section that has house number that in a range of X meter around the intersection and has a hotspot.

Visualization of the solutions

First solution

Seconed Solution

Conlusion

To conlude our findings , we have found 41 locations form the first solution and 211 locations form the second solution , which is more flexiable, that can be used as testing location for the IOT applications in Koln.

Challenges

We are not running the data pipeline here becasue our data has been changed in the mobilithek. This change caused a drop in some coloums that i was planning to use in my analysis Therefore , i used a local version which i have downloaded beforehand.